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The Generation and Validation of a 20-Genes Model Influencing the Prognosis of Colorectal Cancer.
Xie, Xiao-Jun; Liu, Ping; Cai, Chu-Dong; Zhuang, Ying-Ru; Zhang, Li; Zhuang, Hai-Wen.
Afiliação
  • Xie XJ; Department of General Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China.
  • Liu P; Department of Pathology, Shiyan Taihe Hospital, Hubei University of Medicine, Shiyan City, Hubei Province, China.
  • Cai CD; Department of General Surgery, Shantou Central Hospital, Shantou, China.
  • Zhuang YR; Department of Anorectal Surgery, Shantou Hospital of TCM, Shantou, China.
  • Zhang L; Intensive Care Unit, Hubei Cancer Hospital, Wuhan, China.
  • Zhuang HW; Division of Gastrointestinal Surgery, Department of General Surgery, The Affiliated Huai'an Hospital of Xuzhou Medical University and The Second People's Hospital of Huai'an, Huai'an, China.
J Cell Biochem ; 118(11): 3675-3685, 2017 11.
Article em En | MEDLINE | ID: mdl-28370286
Colorectal cancer is a common malignant tumor with high incidence affecting the digestive system. This study aimed to identify the key genes relating to prognosis of colorectal cancer and to construct a prognostic model for its risk evaluation. Gene expression profiling of colorectal cancer patients, GSE17537, was downloaded from Gene Expression Omnibus database (GEO). A total of 55 samples from patients ranging from stages 1 to 4 were available. Differentially expressed genes were screened, with which single factor survival analysis was performed to identify the response genes. Interacting network and KEGG enrichment analysis of responsive genes were performed to identify key genes. In return, Fisher enrichment analysis, literature mining, and Kaplan-Meier analysis were used to verify the effectiveness of the prognostic model. The 20-gene model generated in this study posed significant influences on the prognoses (P = 9.691065e-09). Significance was verified via independent dataset GSE38832 (P = 9.86581e-07) and GSE17536 (P = 2.741e-08). The verified effective 20-gene model could be utilized to predict prognosis of patients with colorectal cancer and would contribute to post-operational treatment and follow-up strategies. J. Cell. Biochem. 118: 3675-3685, 2017. © 2017 Wiley Periodicals, Inc.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Colorretais / Regulação Neoplásica da Expressão Gênica / Bases de Dados Genéticas / Modelos Genéticos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Colorretais / Regulação Neoplásica da Expressão Gênica / Bases de Dados Genéticas / Modelos Genéticos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article